Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynamics of different water flow scenarios, which is not scalable for a large network of sensors. This paper presents a novel approach to model spatio-temporal dependencies across river network stations using a partial correlation graph. We also provide a method to enrich this partial correlation graph by eliminating the spurious correlations. We demonstrate the utility of enriched partial correlation graphs in multivariate forecasting for various scenarios and state-of-the-art multivariate forecasting models. We observe that the forecasting techniques that use information from the enriched partial correlation graph outperform standard time series forecasting approaches for river network forecasting.

各种时间序列预测方法已成功应用于水位预测问题。图形时间序列模型是一类多变量时间序列，用于对传感器之间的时空依赖性进行建模。构建基于图的模型涉及数据预处理和相关性分析，以捕捉不同水流场景的动态，这对于大型传感器网络来说是不可扩展的。本文提出了一种使用偏相关图来模拟跨河网站点的时空依赖性的新方法。我们还提供了一种通过消除虚假相关来丰富此部分相关图的方法。我们展示了丰富的偏相关图在各种场景和最先进的多元预测模型的多元预测中的效用。